Asynchronous compute, AMD, Nvidia, and DX12: What we know so far

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Ever since DirectX 12 was announced, AMD and Nvidia have jockeyed for position regarding which of them would offer better support for the new API and its various features. One capability that AMD has talked up extensively is GCN’s support for asynchronous compute. Asynchronous compute allows all GPUs based on AMD’s GCN architecture to perform graphics and compute workloads simultaneously. Last week, an Oxide Games employee reported that contrary to general belief, Nvidia hardware couldn’t perform asynchronous computing and that the performance impact of attempting to do so was disastrous on the company’s hardware.

This announcement kicked off a flurry of research into what Nvidia hardware did and did not support, as well as anecdotal claims that people would (or already did) return their GTX 980 Ti’s based on Ashes of the Singularity performance. We’ve spent the last few days in conversation with various sources working on the problem, including Mahigan and CrazyElf at Overclock.net, as well as parsing through various data sets and performance reports. Nvidia has not responded to our request for clarification as of yet, but here’s the situation as we currently understand it.

Nvidia, AMD, and asynchronous compute

When AMD and Nvidia talk about supporting asynchronous compute, they aren’t talking about the same hardware capability. The Asynchronous Command Engines in AMD’s GPUs (between 2-8 depending on which card you own) are capable of executing new workloads at latencies as low as a single cycle. A high-end AMD card has eight ACEs and each ACE has eight queues. Maxwell, in contrast, has two pipelines, one of which is a high-priority graphics pipeline. The other has a a queue depth of 31 — but Nvidia can’t switch contexts anywhere near as quickly as AMD can.

According to a talk given at GDC 2015, there are restrictions on Nvidia’s preeemption capabilities. Additional text below the slide explains that “the GPU can only switch contexts at draw call boundaries” and “On future GPUs, we’re working to enable finer-grained preemption, but that’s still a long way off.” To explore the various capabilities of Maxwell and GCN, users at Beyond3D and Overclock.net have used an asynchronous compute tests that evaluated the capability on both AMD and Nvidia hardware. The benchmark has been revised multiple times over the week, so early results aren’t comparable to the data we’ve seen in later runs.

Note that this is a test of asynchronous compute latency, not performance. This doesn’t test overall throughput — in other words, just how long it takes to execute — and the test is designed to demonstrate if asynchronous compute is occurring or not. Because this is a latency test, lower numbers (closer to the yellow “1” line) mean the results are closer to ideal.

Here’s the R9 290’s performance. The yellow line is perfection — that’s what we’d get if the GPU switched and executed instantaneously. The y-axis of the graph shows normalized performance to 1x, which is where we’d expect perfect asynchronous latency to be. The red line is what we are most interested in. It shows GCN performing nearly ideally in the majority of cases, holding performance steady even as thread counts rise. Now, compare this to Nvidia’s GTX 980 Ti.

Attempting to execute graphics and compute concurrently on the GTX 980 Ti causes dips and spikes in performance and little in the way of gains. Right now, there are only a few thread counts where Nvidia matches ideal performance (latency, in this case) and many cases where it doesn’t. Further investigation has indicated that Nvidia’s asynch pipeline appears to lean on the CPU for some of its initial steps, whereas AMD’s GCN handles the job in hardware.

Right now, the best available evidence suggests that when AMD and Nvidia talk about asynchronous compute, they are talking about two very different capabilities. “Asynchronous compute,” in fact, isn’t necessarily the best name for what’s happening here. The question is whether or not Nvidia GPUs can run graphics and compute workloads concurrently. AMD can, courtesy of its ACE units.

It’s been suggested that AMD’s approach is more like Hyper-Threading, which allows the GPU to work on disparate compute and graphics workloads simultaneously without a loss of performance, whereas Nvidia may be leaning on the CPU for some of its initial setup steps and attempting to schedule simultaneous compute + graphics workload for ideal execution. Obviously that process isn’t working well yet. Since our initial article, Oxide has since stated the following:

“We actually just chatted with Nvidia about Async Compute, indeed the driver hasn’t fully implemented it yet, but it appeared like it was. We are working closely with them as they fully implement Async Compute.”

Here’s what that likely means, given Nvidia’s own presentations at GDC and the various test benchmarks that have been assembled over the past week. Maxwell does not have a GCN-style configuration of asynchronous compute engines and it cannot switch between graphics and compute workloads as quickly as GCN. According to Beyond3D user Ext3h:

“There were claims originally, that Nvidia GPUs wouldn’t even be able to execute async compute shaders in an async fashion at all, this myth was quickly debunked. What become clear, however, is that Nvidia GPUs preferred a much lighter load than AMD cards. At small loads, Nvidia GPUs would run circles around AMD cards. At high load, well, quite the opposite, up to the point where Nvidia GPUs took such a long time to process the workload that they triggered safeguards in Windows. Which caused Windows to pull the trigger and kill the driver, assuming that it got stuck.

“Final result (for now): AMD GPUs are capable of handling a much higher load. About 10x times what Nvidia GPUs can handle. But they also need also about 4x the pressure applied before they get to play out there capabilities.”

Ext3h goes on to say that preemption in Nvidia’s case is only used when switching between graphics contexts (1x graphics + 31 compute mode) and “pure compute context,” but claims that this functionality is “utterly broken” on Nvidia cards at present. He also states that while Maxwell 2 (GTX 900 family) is capable of parallel execution, “The hardware doesn’t profit from it much though, since it has only little ‘gaps’ in the shader utilization either way. So in the end, it’s still just sequential execution for most workload, even though if you did manage to stall the pipeline in some way by constructing an unfortunate workload, you could still profit from it.”

Nvidia, meanwhile, has represented to Oxide that it can implement asynchronous compute, however, and that this capability was not fully enabled in drivers. Like Oxide, we’re going to wait and see how the situation develops. The analysis thread at Beyond3D makes it very clear that this is an incredibly complex question, and much of what Nvidia and Maxwell may or may not be doing is unclear.

Earlier, we mentioned that AMD’s approach to asynchronous computing superficially resembled Hyper-Threading. There’s another way in which that analogy may prove accurate: When Hyper-Threading debuted, many AMD fans asked why Team Red hadn’t copied the feature to boost performance on K7 and K8. AMD’s response at the time was that the K7 and K8 processors had much shorter pipelines and very different architectures, and were intrinsically less likely to benefit from Hyper-Threading as a result. The P4, in contrast, had a long pipeline and a relatively high stall rate. If one thread stalled, HT allowed another thread to continue executing, which boosted the chip’s overall performance.

GCN-style asynchronous computing is unlikely to boost Maxwell performance, in other words, because Maxwell isn’t really designed for these kinds of workloads. Whether Nvidia can work around that limitation (or implement something even faster) remains to be seen.

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